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Production Principles (aka Factory Physics )

Operations Management Dr. Mark P. Van Oyen Physics should be explained as simply as possible, but no simpler. – Albert Einstein. Production Principles (aka Factory Physics ). Filename: physics-lec .ppt. Objectives.

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Production Principles (aka Factory Physics )

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  1. Operations Management Dr. Mark P. Van Oyen Physics should be explained as simply as possible, but no simpler. – Albert Einstein Production Principles (aka Factory Physics) Filename: physics-lec.ppt

  2. Objectives • Lay a mathematical foundation that guides insight and understanding into the logistical performance of production systems. • Diagnostics to assess whether the logistical efficiency of a production system is • Nearly as good as it can be • Reasonable • Poor

  3. Beset By a Blizzard of Buzzwords • TBC: Time-based Competition • JIT: Just in Time • MRP: Materials Requirements Planning • MRP II: Manufacturing Resources Planning • ERP: Enterprise Resources Planning • GT: Group Technology • CIM: Computer-integrated Manufacturing • FMS: Flexible Manufacturing Systems • OPT: Optimized Production Technology • BPR: Business Process Reengineering • TQM: Total Quality Management • and all the people said … “This too shall pass…”

  4. Bliss Beyond the Buzzwords • Beyond the buzzwords there lies a place … • where systems can be described objectively, • where performance can be quantified or placed within a range of possible values • where principles can be learned for why systems behave the way they do. • Physics, mathematics, logic,production logistics • A 1995 analysis performed by R. Suri at U. Wisconsin, Madison revealed that over 70% of the policies in use by managers were significant obstacles to achieving Quick Response Manufacturing (QRM) - and those managers did not even realize that their good intentions were hurting the company.

  5. Summary of JIT: Advantages of Pull • Advantages: • Observability: we can see WIP but not capacity. • Efficiency: pull systems require less average WIP to attain same throughput as equivalent push system. • Robustness: pull systems are less sensitive to errors in WIP level than push systems are to errors in release rate. • Quality: pull systems require and promote improved quality. • Magic of Pull: WIP Cap. Pull limits the amount of WIP released to the system. It prevents large queues which lead to long cycle times.

  6. An Approach that will Help Get Us There • CONWIP uses both PULL AND PUSH concepts and can be used with MRP or with a JIT environment between stock points • Pull: • PULL a job into the line when a job exits. • maintains a CONstant amount of Work In Process. • CONWIP count applies to jobs with the same routing. • Push: • PUSH jobs between stations on same line. • PUSH jobs into buffer storage between lines. (inventory or stock points between CONWIP lines) Total # jobs in line is constant = WIP. Release a job ONLY when one exits.

  7. CONWIP works really well! FYI Only • CONWIP vs. Push: • Easier to implement and more robust control – can be implemented where kanban cannot! • Less congestion. • Greater predictability. • CONWIP vs. Kanban: • Can accommodate a changing product mix. • Can be used with setups. • Suitable for short runs of small lots. • More predictable. • Fewer card counts to set. • Can accommodate floating bottlenecks. • Less pacing stress on the workers :-)

  8. Physics for Production Design? • Requirements: • 3000 units per day throughput, • with a cycle time of not greater than 10 days, • Can we do it? How much WIP will be needed? • Answer: • Who knows? • An exercise for you: • Draw CT vs. WIP • Draw graph of TH vs. WIP ?

  9. TH, Repeat for CT Do You Know the Basic Laws? • For example, consider a serial production line with 4 stations (there may or may not be a bottleneck) • 1. How does TH (throughput) depend on average WIP? • 2. How does CT (cycle time) depend on average WIP? WIP.

  10. The Basic Laws Factory Physics

  11. Factory Physics • Structure: Plant is made up of routings (lines), which in turn are made up of a series of workstations that execute processes. • Focus: Factory Physics is concerned with the network and flows at the routing (line) level. Fundamental structure to be analyzed is a line. 1 n prod. Rate rn N

  12. Parameters: Just 2 ! • Descriptors of a Line: • 1) Bottleneck Rate (rb):Rate (jobs/hr) of the workstation having the highest long-term utilization (the fraction of time either working or broken down). This is usually the slowest rate of any station. • 2) Raw Total Process Time (T0):Sum of the long-term averageprocess times of each station in the line. • Note: • It’s amazingly simple! • Lines with same rb and T0 can behave very differently depending on how much variability they have.

  13. Congestion – 3 Levels – 3 Models “The Good, The Bad, & The Ugly.” • Congestion Level in the Line: • We will construct 3 models of a line with the same ngestion. • “Best Case” = Zero variability case • “Practical worst case,” (service times have CV = 1) • “Worst possible case,” (high CV’s, batch transfer lots) • Outline: • 1. BC (good), • 2. WC (bad), then • 3. PWC (ugly)

  14. The KEY Manufacturing Law • Little's Law: The fundamental relation between WIP, CT, and TH over the long-term is: • WIP = TH * CT • jobs = (j/hr.) * hr • Relationships hold on average in the long run • Key assumption: jobs are not created or destroyed within the line • Be consistent in your accounting (either all jobs or only acceptable-quality jobs)

  15. Parameters (cont.) • Critical WIP (W0): WIP level in which a line having no congestion would achieve maximum throughput (i.e., rb) with minimum cycle time (i.e., T0). • W0 = rbT0 • If you want to know WHY? To make sense of this, apply Little’s Law WIP = TH * CT, where we have fixed TH=rb & CT = T0

  16. 3 Performance Benchmark Models • Q: How do we know if a particular production system or service operation is well-managed? • A: We can compare its performance to physics-based models that demonstrate what can be achieved • 1. Best Case Analysis • 2. Practical Worst Case Analysis (presented last) • 3. Worst Case Analysis

  17. The Penny Fab • Characteristics: • Four identical stations in series. • Each takes 2 hours per job (penny). • No variability. • CONWIP job releases - A fresh job is released only after a completed job has exited the system • Parameters: rb = T0 = W0 = 0.5 pennies/hour 8 hours 0.5  8 = 4 pennies

  18. The Penny Fab

  19. The Penny Fab Simulation

  20. TH vs. WIP: Best Case rb 1/T0 W0

  21. CT vs. WIP: Best Case 1/rb T0 W0

  22. Best Case Performance • Best Case Law: When equality is achieved in the above bound, the minimum cycle time (CTbest) for a given WIP level is given by The maximum throughput (THbest) for a given WIP level is given by,

  23. Best Case Performance (cont.) • Example: For Penny Fab, rb = 0.5 and T0 = 8, so W0 = 0.5  8 = 4, which are exactly the curves we plotted. • Optional! - the following slides derive these results.

  24. What causes less-than Best Case Performance? • Time in System • = process time + queueing time (variability & batching) + set-up time + waiting time (assembly).In Goldratt terminology! • Having batch sizes (or transfer-lot sizes) > 1 (even without variability! This is a type of waiting) • Having processing time variability (this causes queueing!) • Mismatched completion times of semi-finished goods awaiting assembly • Long set-up times • Inconsistency between planning model (ERP/MRP) and the actual behavior of the operation • Poor management that fails to coordinate the overall production flow.

  25. Worst Case • Observation: The Best Case yields the minimum cycle time and maximum throughput for each WIP level. • Question: What conditions would cause the maximum cycle time and minimum throughput? • Experiment: • set average process times same as Best Case (so rb and T0 are unchanged) • follow a marked job through system • imagine marked job experiences maximum queueing(this job will be marked green - it will wait for all others)

  26. TH vs. WIP: Worst Case Best Case rb Worst Case 1/T0 W0

  27. CT vs. WIP: Worst Case Worst Case Best Case T0 W0

  28. Worst Case Performance • Worst Case Law: The worst case cycle time for a given WIP level is given by, • CTworst = WIP T0 The worst case throughput for a given WIP level is given by, • THworst = 1 / T0 • Randomness? None - perfectly predictable, but bad! • Is it contrived? Well, it is very common for systems to have “move batches” or transfer lots that create this sort of behavior!Moreover, we like production systems to be deterministic!

  29. Practical Worst Case • Observation: There is a BIG GAP between the Best Case and Worst Case performance. • Question: Can we find an intermediate case that: • divides “good” and “bad” lines, and • is computable? • Experiment: consider a line with a given rb and T0 and: • single machine stations • balanced lines • variability such that all WIP configurations (states) are equally likely

  30. Practical Worst Case (PWC) Performance • Practical Worst Case Definition: The practical worst case (PWC) cycle time for a given WIP level is given by, The PWC throughput for a given WIP level is given by, where W0 is the critical WIP.

  31. THvs.WIP:Caging the Tiger Best Case rb PWC “Good” Worst Case “Bad” 1/T0 W0

  32. CTvs.WIP:Caging the Tiger Worst Case PWC “Bad” Best Case “Good” T0 W0

  33. Penny Fab Two 2 hr 5 hr 3 hr 10 hr

  34. Penny Fab Two rb = T0 = WIP0 = Answers: 0.4, 2+5+10+3=20, 8

  35. Penny Fab Two - Performance Note: process times in PF2 have var equal to PWC. But… unlike PWC, it has unbalanced line and multi machine stations. Best Case (BC) rb Penny Fab 2 Practical Worst Case (PWC) 1/T0 Worst Case W0

  36. Penny Fab Two - Performance (cont.) Worst Case Practical Worst Case Penny Fab 2 1/rb T0 Best Case W0

  37. 80 70 Practical Worst Case Penny Fab 2 60 MRP uses FIXED lead times 50 CT 40 30 T0 1/rb 20 Best Case 10 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 W0 WIP What’s Wrong at the Heart of MRP? MRP uses FIXED lead times that depend ONLY on the part type, not on the conditions of the factory floor. Plant capacity is assumed infinite!

  38. CT Performance Models • Best Case: • Practical Worst Case: • where W0 = rbT0is the critical WIP. Worst Case: CTworst = WIP T0

  39. TH Performance Models • Best Case • Practical Worst Case: • where W0 = rbT0is the critical WIP. Worst Case: THworst = 1 / T0

  40. Internal Benchmarking Example

  41. Internal Benchmarking Example (cont.) • Critical WIP: rbT0 = 126.5  33.1 = 4187 • Actual Values: • CT = 34 days = 816 hours • WIP = 37400 panels • TH = 45.8 panels/hour • Conclusions: • Throughput is 36% of capacity • WIP is 8.9 times critical WIP • CT is 24.6 times raw process time

  42. Internal Benchmarking Example (cont.) • WIP, w, Required for PWC to Achieve TH = 0.36rb: • TH Resulting from PWC with WIP = 37,400: • Conclusion: actual system is much worse than PWC. Much lower than actual WIP! Much higher than actual TH!

  43. Internal Benchmarking Outcome 140 Best Case 120 "Good" Region Practical Worst Case 100 80 TH (panels/hr) 60 "Bad" Region 40 Actual Performance 20 0 Worst Case -20 0 10000 20000 30000 40000 50000 WIP (panels)

  44. Factory Dynamics Takeaways • Performance Measures: • throughput, TH • WIP • cycle time, CT • service level or fill rate (more complex) • Range of Cases: • best case • practical worst case • worst case • Diagnostics: • simple assessment based on rb, T0, actual WIP,actual TH • evaluate relative to practical worst case

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